





'''simple docstring'''






import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class 		lowerCamelCase	(   _A   , _A   , unittest.TestCase							):
	snake_case_   = IFPipeline
	snake_case_   = TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
	snake_case_   = TEXT_TO_IMAGE_BATCH_PARAMS
	snake_case_   = PipelineTesterMixin.required_optional_params - {"latents"}



	def 	_lowerCamelCase     ( self						):
			return self._get_dummy_components()



	def 	_lowerCamelCase     ( self		,   a_		,   a_=0						):
			if str(a_						).startswith("mps"						):
					lowerCAmelCase  :     Any       						= torch.manual_seed(a_						)
			else:
					lowerCAmelCase  :     Optional[Any]       						= torch.Generator(device=a_						).manual_seed(a_						)

			lowerCAmelCase  :     List[str]       						= {
			    "prompt": "A painting of a squirrel eating a burger",
			    "generator": generator,
			    "num_inference_steps": 2,
			    "output_type": "numpy",
			}

			return inputs



	def 	_lowerCamelCase     ( self						):
			self._test_save_load_optional_components()



	@unittest.skipIf(torch_device != "cuda"		,   reason="float16 requires CUDA"						)
	def 	_lowerCamelCase     ( self						):
			# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
			super().test_save_load_floataa(expected_max_diff=1e-1						)



	def 	_lowerCamelCase     ( self						):
			self._test_attention_slicing_forward_pass(expected_max_diff=1e-2						)



	def 	_lowerCamelCase     ( self						):
			self._test_save_load_local()



	def 	_lowerCamelCase     ( self						):
			self._test_inference_batch_single_identical(
			    expected_max_diff=1e-2		,   )





	@unittest.skipIf(
	    torch_device != "cuda" or not is_xformers_available()		,   reason="XFormers attention is only available with CUDA and `xformers` installed"		,   )
	def 	_lowerCamelCase     ( self						):
			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)






@slow
@require_torch_gpu
class 		lowerCamelCase	(   unittest.TestCase							):



	def 	_lowerCamelCase     ( self						):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()



	def 	_lowerCamelCase     ( self						):
			# if

			lowerCAmelCase  :     str       						= IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"		,   variant="fp16"		,   torch_dtype=torch.floataa						)

			lowerCAmelCase  :     Union[str, Any]       						= IFSuperResolutionPipeline.from_pretrained(
			    "DeepFloyd/IF-II-L-v1.0"		,   variant="fp16"		,   torch_dtype=torch.floataa		,   text_encoder=a_		,   tokenizer=a_						)

			# pre compute text embeddings and remove T5 to save memory

			pipe_a.text_encoder.to("cuda"						)

			lowerCAmelCase						,		lowerCAmelCase  :     Optional[int]       						= pipe_a.encode_prompt("anime turtle"		,   device="cuda"						)

			del pipe_a.tokenizer
			del pipe_a.text_encoder
			gc.collect()

			lowerCAmelCase  :     Optional[int]       						= None
			lowerCAmelCase  :     Optional[int]       						= None

			pipe_a.enable_model_cpu_offload()
			pipe_a.enable_model_cpu_offload()

			pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)
			pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)

			self._test_if(a_		,   a_		,   a_		,   a_						)

			pipe_a.remove_all_hooks()
			pipe_a.remove_all_hooks()

			# img2img

			lowerCAmelCase  :     str       						= IFImgaImgPipeline(**pipe_a.components						)
			lowerCAmelCase  :     List[str]       						= IFImgaImgSuperResolutionPipeline(**pipe_a.components						)

			pipe_a.enable_model_cpu_offload()
			pipe_a.enable_model_cpu_offload()

			pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)
			pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)

			self._test_if_imgaimg(a_		,   a_		,   a_		,   a_						)

			pipe_a.remove_all_hooks()
			pipe_a.remove_all_hooks()

			# inpainting

			lowerCAmelCase  :     Optional[Any]       						= IFInpaintingPipeline(**pipe_a.components						)
			lowerCAmelCase  :     Tuple       						= IFInpaintingSuperResolutionPipeline(**pipe_a.components						)

			pipe_a.enable_model_cpu_offload()
			pipe_a.enable_model_cpu_offload()

			pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)
			pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)

			self._test_if_inpainting(a_		,   a_		,   a_		,   a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   a_						):
			# pipeline 1

			_start_torch_memory_measurement()

			lowerCAmelCase  :     Union[str, Any]       						= torch.Generator(device="cpu"						).manual_seed(0						)
			lowerCAmelCase  :     int       						= pipe_a(
			    prompt_embeds=a_		,   negative_prompt_embeds=a_		,   num_inference_steps=2		,   generator=a_		,   output_type="np"		,   )

			lowerCAmelCase  :     Optional[int]       						= output.images[0]

			assert image.shape == (64, 64, 3)

			lowerCAmelCase  :     Union[str, Any]       						= torch.cuda.max_memory_allocated()
			assert mem_bytes < 13 * 10**9

			lowerCAmelCase  :     Union[str, Any]       						= load_numpy(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"						)
			assert_mean_pixel_difference(a_		,   a_						)

			# pipeline 2

			_start_torch_memory_measurement()

			lowerCAmelCase  :     List[str]       						= torch.Generator(device="cpu"						).manual_seed(0						)

			lowerCAmelCase  :     Tuple       						= floats_tensor((1, 3, 64, 64)		,   rng=random.Random(0						)						).to(a_						)

			lowerCAmelCase  :     str       						= pipe_a(
			    prompt_embeds=a_		,   negative_prompt_embeds=a_		,   image=a_		,   generator=a_		,   num_inference_steps=2		,   output_type="np"		,   )

			lowerCAmelCase  :     Union[str, Any]       						= output.images[0]

			assert image.shape == (256, 256, 3)

			lowerCAmelCase  :     str       						= torch.cuda.max_memory_allocated()
			assert mem_bytes < 4 * 10**9

			lowerCAmelCase  :     Optional[Any]       						= load_numpy(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"						)
			assert_mean_pixel_difference(a_		,   a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   a_						):
			# pipeline 1

			_start_torch_memory_measurement()

			lowerCAmelCase  :     Tuple       						= floats_tensor((1, 3, 64, 64)		,   rng=random.Random(0						)						).to(a_						)

			lowerCAmelCase  :     str       						= torch.Generator(device="cpu"						).manual_seed(0						)

			lowerCAmelCase  :     Dict       						= pipe_a(
			    prompt_embeds=a_		,   negative_prompt_embeds=a_		,   image=a_		,   num_inference_steps=2		,   generator=a_		,   output_type="np"		,   )

			lowerCAmelCase  :     int       						= output.images[0]

			assert image.shape == (64, 64, 3)

			lowerCAmelCase  :     Optional[Any]       						= torch.cuda.max_memory_allocated()
			assert mem_bytes < 10 * 10**9

			lowerCAmelCase  :     int       						= load_numpy(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"						)
			assert_mean_pixel_difference(a_		,   a_						)

			# pipeline 2

			_start_torch_memory_measurement()

			lowerCAmelCase  :     Tuple       						= torch.Generator(device="cpu"						).manual_seed(0						)

			lowerCAmelCase  :     Any       						= floats_tensor((1, 3, 256, 256)		,   rng=random.Random(0						)						).to(a_						)
			lowerCAmelCase  :     List[str]       						= floats_tensor((1, 3, 64, 64)		,   rng=random.Random(0						)						).to(a_						)

			lowerCAmelCase  :     Dict       						= pipe_a(
			    prompt_embeds=a_		,   negative_prompt_embeds=a_		,   image=a_		,   original_image=a_		,   generator=a_		,   num_inference_steps=2		,   output_type="np"		,   )

			lowerCAmelCase  :     int       						= output.images[0]

			assert image.shape == (256, 256, 3)

			lowerCAmelCase  :     Optional[Any]       						= torch.cuda.max_memory_allocated()
			assert mem_bytes < 4 * 10**9

			lowerCAmelCase  :     Dict       						= load_numpy(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"						)
			assert_mean_pixel_difference(a_		,   a_						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   a_						):
			# pipeline 1

			_start_torch_memory_measurement()

			lowerCAmelCase  :     Any       						= floats_tensor((1, 3, 64, 64)		,   rng=random.Random(0						)						).to(a_						)
			lowerCAmelCase  :     Any       						= floats_tensor((1, 3, 64, 64)		,   rng=random.Random(1						)						).to(a_						)

			lowerCAmelCase  :     List[str]       						= torch.Generator(device="cpu"						).manual_seed(0						)
			lowerCAmelCase  :     Union[str, Any]       						= pipe_a(
			    prompt_embeds=a_		,   negative_prompt_embeds=a_		,   image=a_		,   mask_image=a_		,   num_inference_steps=2		,   generator=a_		,   output_type="np"		,   )

			lowerCAmelCase  :     Optional[Any]       						= output.images[0]

			assert image.shape == (64, 64, 3)

			lowerCAmelCase  :     List[Any]       						= torch.cuda.max_memory_allocated()
			assert mem_bytes < 10 * 10**9

			lowerCAmelCase  :     Dict       						= load_numpy(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"						)
			assert_mean_pixel_difference(a_		,   a_						)

			# pipeline 2

			_start_torch_memory_measurement()

			lowerCAmelCase  :     List[Any]       						= torch.Generator(device="cpu"						).manual_seed(0						)

			lowerCAmelCase  :     Union[str, Any]       						= floats_tensor((1, 3, 64, 64)		,   rng=random.Random(0						)						).to(a_						)
			lowerCAmelCase  :     Optional[int]       						= floats_tensor((1, 3, 256, 256)		,   rng=random.Random(0						)						).to(a_						)
			lowerCAmelCase  :     Any       						= floats_tensor((1, 3, 256, 256)		,   rng=random.Random(1						)						).to(a_						)

			lowerCAmelCase  :     Tuple       						= pipe_a(
			    prompt_embeds=a_		,   negative_prompt_embeds=a_		,   image=a_		,   mask_image=a_		,   original_image=a_		,   generator=a_		,   num_inference_steps=2		,   output_type="np"		,   )

			lowerCAmelCase  :     List[str]       						= output.images[0]

			assert image.shape == (256, 256, 3)

			lowerCAmelCase  :     List[Any]       						= torch.cuda.max_memory_allocated()
			assert mem_bytes < 4 * 10**9

			lowerCAmelCase  :     Tuple       						= load_numpy(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"						)
			assert_mean_pixel_difference(a_		,   a_						)






def   __A    (				):
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()










'''simple docstring'''






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for data in source_data:
				for i, el in enumerate(a_						):
						if len(a_						) < i + 1:
								data_lists.append([]						)
						data_lists[i].append(float(a_						)						)
		return data_lists






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for dlist, weight in zip(a_   ,a_						):
				lowerCAmelCase  :     Optional[Any]       						= min(a_						)
				lowerCAmelCase  :     List[str]       						= max(a_						)

				lowerCAmelCase  :     list[float]       						= []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))						)
								except ZeroDivisionError:
										score.append(1						)

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)						)
								except ZeroDivisionError:
										score.append(0						)

        # weight not 0 or 1
				else:
						lowerCAmelCase  :     List[Any]       						= f'''Invalid weight of {weight:f} provided'''
						raise ValueError(a_						)

				score_lists.append(a_						)

		return score_lists






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[float]       						= [0 for i in range(len(score_lists[0]						)						)]

		for slist in score_lists:
				for j, ele in enumerate(a_						):
						lowerCAmelCase  :     Optional[Any]       						= final_scores[j] + ele

		return final_scores






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     Union[str, Any]       						= get_data(a_						)
		lowerCAmelCase  :     List[Any]       						= calculate_each_score(a_   ,a_						)
		lowerCAmelCase  :     Optional[Any]       						= generate_final_scores(a_						)

		# append scores to source data
		for i, ele in enumerate(a_						):
				source_data[i].append(a_						)

		return source_data



